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March 6, 2026 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Echo Pyramid enables smart voice interaction applications on M5Stack Atom ESP32 IoT controllers

Designed for M5Stack Atom, AtomS3, and AtomS3R series IoT controllers based on ESP32 or ESP32-S3 wireless SoC, the Echo Pyramid base enables smart voice interaction applications such as far-field voice recognition, voice assistants, voice control, and more. The device features a built-in speaker, a MEMS microphone, an ES8311 HD audio codec for playback and capture, and an STM32 MCU for touch areas and RGB LED management. It’s powered via a USB Type-C port and can be expanded through a 4-pin connector for I2C modules. Echo Pyramic specifications: Supported IoT controllers – M5Stack Atom, AtomS3, and AtomS3R Microcontroller – STMicro STM32G030F6P6 32-bit Arm Cortex-M0+ CPU @ 64 MHz with 8KB SRAM, 64 KB flash Audio HD Codec – ES8311, handles playback and recording Microphone – LMA3729T381-0Y3S MEMS microphone ADC – ES7210 for microphone input Built-in speaker on the bottom of the pyramid Amplifier – AW87559 Class-D speaker driver for the speaker […]

February 22, 2026 by Jean-Luc Aufranc (CNXSoft) - 15 Comments

Taalas HC1 hardwired Llama-3.1 8B AI accelerator delivers up to 17,000 tokens/s

Taalas HC1 is an AI accelerator hardwired (i.e, implemented in hardware) with Llama-3.1 8B and delivering close to 17,000 tokens/s of AI performance with the model, outperforming datacenter accelerators such as NVIDIA B200 or Cerebras chips. The Taalas HC1 is about 10x faster than the Cerebras chip, costs 20x less to build, and consumes 10x less power. The main downside is that it only works with the model hardwired into the hardware, currently Llama-3.1 8B, although we’re told it “retains flexibility through configurable context window size and support for fine-tuning via low-rank adapters (LoRAs)”. Hardware accelerators usually come with memory on one side and compute on the other. Both operate at different speeds, and the memory bandwidth is usually the bottleneck for Large Language Models. Taalas technology unifies storage and compute on a single chip, at DRAM-level density, to massively increase the performance and reduce power consumption. Ultra-fast inference can […]

February 13, 2026February 13, 2026 by Jean-Luc Aufranc (CNXSoft) - No Comments

Mimiclaw is an OpenClaw-like AI assistant for ESP32-S3 boards

MimiClaw is an OpenClaw-inspired AI assistant designed for ESP32-S3 boards, which acts as a gateway between the Telegram messaging application and Claude online LLM to control the hardware by just chatting to it. We’ve just written about PicoClaw, an ultra-lightweight personal AI Assistant for cheap Linux boards that just needs 10MB of spare RAM. It was itself inspired by Nanobot, a lightweight assistant written in Python, that’s 99% smaller, in terms of lines of code, than the original OpenClaw project that started it all. Since most of the processing is done through messaging apps and online LLMs, it was only a matter of time until this type of solution was ported to microcontrollers. MimiClaw highlights: Written in C; relies on the ESP-IDF 5.5 framework System requirements – ESP32-S3 board with 16 MB flash and 8 MB PSRAM, such as the LILYGO T7-S3, FireBeetle 2 ESP32-S3, ESP32-S3-DevKitC-1-N16R8, Seeed Studio’s XIAO ESP32S3 […]

February 6, 2026February 6, 2026 by Jean-Luc Aufranc (CNXSoft) - 9 Comments

$199+ M5Stack AI Pyramid Computing Box Linux AI mini PC is based on Axera AX8850 SoC

Axera AX8850 Linux Mini PC 4GB RAM Transparent case

M5Stack has just launched the “AI Pyramid Computer Box” AI mini PC powered by an Axera AX8850 octa-core Cortex-A55 SoC with a 24 TOPS NPU and featuring an unusual design since it’s housed in a pyramidal enclosure. The mini PC features 4GB or 8GB of RAM, 32GB eMMC flash, a microSD card slot, two HDMI 2.0 ports (see specs for details), a 4-mic array, a built-in speaker, two Gigabit Ethernet ports, four USB 3.2 ports, and two USB-C ports for data and power. It serves as a desktop-grade Edge AI computing platform for systems such as AI vision gateways, smart security systems, and local smart photo albums. M5Stack AI Pyramid Computing Box specifications: SoC – Axera AX8850 CPU – Octa-core Cortex‑A55 processor at 1.7 GHz NPU – 24 TOPS @ INT8 VPU Video Encoder – 8K @ 30 fps H.264/H.265 encoding, supports scaling / cropping Video Decoder – 8K @ 60 fps H.264/H.265 decoding, supports 16 channels 1080p parallel decoding, […]

February 1, 2026February 1, 2026 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Fusion HAT+ Review – Adding AI voice and servo/motor control to Raspberry Pi for robotics, Smart Home, or education

Fusion HAT+ review LLM servo control camera

SunFounder has sent me a review sample of the Fusion HAT+ Raspberry Pi expansion board designed for motor and servo control using audio interactions with its built-in microphone and speaker, as well as LLM models. It can be used as an AI-enabled robot controller, a smart home hub, a voice assistant, or an interactive learning platform. In this review, after an unboxing and going through the installation of the Fusion HAT+ on a Raspberry Pi 5 2GB, I’ll mainly focus on the voice interaction part using text-to-speech (TTS), speech-to-text (STT), and local and cloud-based LLMs and VLMs, and also quickly test servo control to wave a flag using voice commands. SunFounder Fusion HAT+ unboxing I received the sample in the retail package reading “SunFounder Fusion HAT+ for Raspberry Pi” and detailing the key features, namely rechargeable battery, 12x PWM, onboard speaker and microphone, 4x 12-bit ADC, safe shutdown, 4x DC […]

January 31, 2026January 31, 2026 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

M5Stack AI-8850 LLM Accelerator M.2 Kit offers an alternative to Raspberry Pi AI HAT+ 2

M5Stack has launched the “AI-88502 LLM Accelerator M.2 Kit 8GB Version” based on its LLM-8850 M.2 card with a 24 TOPS Axera AX8850 SoC, and offering an alternative to the Raspberry Pi AI HAT+ 2, supporting both LLM and AI vision workloads. The kit is comprised of the M.2 card and a Raspberry Pi-HAT 8850 board with USB PD power input for the card and Raspberry Pi 5, a 16-pin PCIe connector and 40-pin GPIO header for connection to the SBC, as well as accessories. M5Stack AI-8850 LLM accelerator M.2 kit specifications: M5Stack LLM‑8850 M.2 card SoC – Axera AX8850 CPU – Octa-core Cortex‑A55 processor at 1.7 GHz NPU – 24 TOPS @ INT8 VPU Video Encoder – 8K @ 30 fps H.264/H.265 encoding, supports scaling / cropping Video Decoder – 8K @ 60 fps H.264/H.265 decoding, supports 16 channels 1080p parallel decoding, supports scaling / cropping Memory (two options) 8GB 64‑bit LPDDR4x @ 4266 Mbps 4GB 64-bit LPDDR4x, 4266 Mbps (not […]

January 27, 2026January 27, 2026 by Jean-Luc Aufranc (CNXSoft) - No Comments

MaixCAM2 modular 4K AI camera is based on Axera AX630 SoC with 3.2 TOPS NPU (Crowdfunding)

Designed for makers and researchers, Sipeed’s MaixCAM2 is a modular AI camera built around an Axera AX630 AI SoC with 3.2 TOPS (INT8) to 12.8 TOPS (INT4) of AI performance, yet delivering 10 to 20x higher performance on OpenMV-N6 and Raspberry Pi 5, and about the same performance (140 FPS) as a 33 TOPS Jetson Orin Nano 4GB Super in YOLO11n. The camera comes with 1GB or 4GB RAM, a 32GB eMMC flash, and a microSD card slot for storage, a 2.4-inch touchscreen display, and an 8MP / 4Kp30 camera sensor with LED flash, as well as two microphones and a speaker, making it suitable for local LLM and VLM applications. It also features two PMOD connectors for expansion, such as a thermal imager and a Time of Flight (ToF) ranging sensor. MaxiCAM2 specifications: SoC – Axera Tech AX630 CPU Dual-core Arm Cortex-A53 @ 1.2 GHz running Ubuntu Linux 32-bit […]

January 27, 2026January 28, 2026 by Debashis Das - No Comments

Lanner EAI-I351 – An NVIDIA Jetson Thor Edge AI computer with 100GbE QSFP28 port and 8x GMSL2 camera

Lanner EAI I351 Jetson Thor Edge AI computer with QSFP28 port

Lanner EAI-I351 is a rugged edge AI computer built around the NVIDIA Jetson Thor platform, featuring a 100GbE QSFP28 port for high-bandwidth networking and 8x GMSL2 camera inputs for low-latency vision processing. The system supports up to 128 GB of 256-bit LPDDR5X memory, features up to four 25GbE lanes via a QSFP28 port, a 5GbE RJ45 port, HDMI 2.0, four USB 3.2 Gen1 ports, two RS232/422/485 serial interfaces with optional CANBus, digital I/Os, and audio interfaces. Three expansion sockets are offered with an M.2 NVMe slot, an M.2 E-Key for Wi-Fi/Bluetooth, and an M.2 B-Key for 4G LTE/5G cellular with dual Nano-SIM support. Designed for industrial deployments, the embedded computer can operate in a –25°C to 70°C temperature range and takes 24V/48V DC power input. It targets robotics, smart manufacturing, and autonomous systems such as collaborative robots, AMRs, and machine-vision-based factory automation. Lanner EAI-I351 specifications: Supported system-on-module – NVIDIA Jetson […]